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Legal and Regulatory Challenges of Higher Levels of Automation

Proposals and their research activities may address issues related to the means of approval/certification of novel ATM-related airborne and ground systems that enable higher levels of automation. Proposals may in particular focus on systems based on machine learning techniques. Proposals should follow a holistic approach and consider legal and regulatory aspects including privacy as well as the technical aspects (architecture, system performance, reliability, etc.) of the approval/certification jointly to ensure that the different disciplines are aligned. On the one hand, the research activities could investigate and evaluate approaches that can potentially be applied for the approval and/or certification of automation and that allow to demonstrate the safety of automation during normal, impaired operation and recovery phases of continuous and safe service provision. On the other hand, these research activities could aim at providing guidelines for developing advanced automation in order to simplify approval/certification. Of particular interest is to show how safety can be ensured even if not all situations and variations of parameters can be anticipated during the design phase. Research activities may apply Uncertainty Quantification to address this issue and also cover the specific challenges of certification of automation that can adapt is behaviour to changes of the environment over time.

Research activities shall take into account other initiatives developing safety of life systems that may have different approaches to certification and review their applicability to ATM.

Please note that EASA is developing an Artificial Intelligence (AI) Roadmap planned to be released by mid-2019 which aims at identifying the opportunities, challenges and impact of this emerging technology and to propose a corresponding action plan. Proposals should plan effort to analyse how their research is positioned with respect to the EASA roadmap.

The introduction of advanced automation in ATM is not only a technical challenge, before this technology can be deployed questions of responsibility and liability have to be answered. Additionally, procedures and methods are required for the approval and/or certification of advanced automation. Additional challenges are presented by more advanced automation that applies novel methods like machine learning to learn and adapt its behaviour (in real time) during operation as the exact behaviour of the automation cannot be predicted in advance.

The expected outcome is twofold: Projects are expected to provide guidelines for the designing of advanced automation in line with approval and/or certification requirements and to provide new methods for the approval and/or certification of advanced automation. Both should contribute to enabling high levels of automation in ATM while a keeping safety on a high level.